{"id":"W3018217961","doi":"10.1109/mwc.001.2000010","title":"A Hierarchical Soft RAN Slicing Framework for Differentiated Service Provisioning","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"","keywords":"Computer science; Slicing; Provisioning; Computer network; Quality of service; Distributed computing; Key (lock); Shared resource; Service (business); Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002257651,0.0009193959,0.000612882,0.0009630756,0.0007933828,0.002252011,0.002331863,0.0007608301,0.003606359],"category_scores_gemma":[0.001919016,0.000535526,0.001071427,0.0007912891,0.001062923,0.002124637,0.00203371,0.00188564,0.001003383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001480466,"about_ca_system_score_gemma":0.00241782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007563685,"about_ca_topic_score_gemma":0.01046271,"domain_scores_codex":[0.9989083,0.0002632671,0.00009693555,0.0001566974,0.0004205436,0.0001542846],"domain_scores_gemma":[0.9991616,0.0002117855,0.00009622367,0.0002030823,0.0002304107,0.00009696033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000166721,0.00007865016,0.0008547912,0.0003022632,0.00008251952,0.000580245,0.000541145,0.2775007,0.01734081,0.5319525,0.007190846,0.1634089],"study_design_scores_gemma":[0.00001999565,0.00004780838,0.0001193793,0.00005506328,0.00003436629,0.0001502992,0.00005442374,0.9138547,0.004148828,0.05430791,0.02716696,0.00004024184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001174215,0.0002592328,0.994979,0.00006412344,0.0000354971,0.00006053608,0.00005657676,0.0008723599,0.002498561],"genre_scores_gemma":[0.1125542,0.000724879,0.882628,0.0001035593,0.00008420504,0.0001816154,0.0003841278,0.0003424452,0.002997037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007563685,"threshold_uncertainty_score":0.01503932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06072001826955023,"score_gpt":0.2974205126141442,"score_spread":0.2367004943445939,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}